Sustainability reporting for web-based services often relies on simplified end-user energy models that assume constant laptop power during browser interactions. Energy models such as Digst and DIMPACT apply fixed power values (15-22W), yet the validity of this approach for realistic browsing remains underexplored. We empirically evaluate constant-power assumptions in a controlled user study where ten participants repeatedly complete eight representative user flows across shopping, booking, navigation, and news services on four laptop platforms, while device energy is measured. Typical power is 9--13~W, substantially below current reporting standards, implying systematic overestimation. Moreover, the error scales proportionally with task duration, indicating systematic bias rather than random noise. Comparing progressively refined constant-power models, we find that category-specific parameters improve accuracy more than hardware-only parameters and approach flow-specific performance. The best fit is obtained by combining category (or flow) with hardware, while category-level models retain most of the benefit with fewer parameters, making them a practical upgrade for sustainability reporting.

翻译：网络服务的可持续性报告通常依赖于简化的终端用户能耗模型，这些模型假设笔记本电脑在浏览器交互期间保持恒定功耗。诸如Digst和DIMPACT等能耗模型采用固定功耗值（15-22W），然而这种方法对于真实浏览场景的有效性仍未得到充分探究。我们通过一项受控用户研究，对恒定功耗假设进行了实证评估：十名参与者在四种笔记本电脑平台上，重复完成涵盖购物、预订、导航和新闻服务的八个代表性用户流程，同时测量设备能耗。典型功耗为9–13W，显著低于当前报告标准，这意味着存在系统性高估。此外，误差与任务时长成比例缩放，表明这是系统性偏差而非随机噪声。通过比较逐步细化的恒定功耗模型，我们发现特定类别参数比仅硬件参数更能提高准确性，并且接近流程特定模型的性能。最佳拟合通过结合类别（或流程）与硬件参数获得，而类别级模型能以更少的参数保留大部分优势，这使其成为可持续性报告的一种实用升级方案。